Biometric scale

ABSTRACT

A method for configuring a monitoring component for a user includes receiving an electrocardiograph (ECG) signal from an ECG component, receiving a weight signal from a scale component, and combining features extracted from the ECG signal and the weight signal to generate a current biometric signal. Responsive to the current biometric signal matching a historical biometric signal, the method includes obtaining a user profile and determining a health status for association with the user profile by classifying the current biometric signal using disease models and fitness models.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a divisional of U.S. patent application Ser. No.15/070,737 filed on Mar. 15, 2016, which is a continuation-in-part ofU.S. patent application Ser. No. 14/854,569 filed on Sep. 15, 2015, theentire disclosure of both of which are hereby incorporated by reference.

TECHNICAL FIELD

This disclosure relates to a monitoring component using both weight andother biometric signals to identify a user and track biometricinformation associated with the user.

BACKGROUND

Biometric characteristics have been used to identify unique users forvarious purposes, including access control. These characteristicsconventionally include fingerprints, DNA, retinal maps, facialrecognition, etc., the likes of which are secure but expensiveidentification solutions.

Device users involved in fitness programs or at risk for variousdiseases may wish not only to be easily identified but also to monitorboth weight data and other biometric data, for example, to identifyfitness conditions, risk factors, or disease diagnoses. Means currentlyavailable to capture weight data and other biometric data are present inseparate devices, are overly cumbersome in terms of identificationmethods, connections, wires, etc., or can offer only a single source forbiometric data.

SUMMARY

Disclosed herein is method for configuring a monitoring component for auser. The method includes receiving an electrocardiograph (ECG) signalfrom an ECG component, receiving a weight signal from a scale component,and combining features extracted from the ECG signal and the weightsignal to generate a current biometric signal. Responsive to the currentbiometric signal matching a historical biometric signal, the methodfurther includes obtaining a user profile and determining a healthstatus for association with the user profile by classifying the currentbiometric signal using disease models and fitness models.

Also disclosed herein is a system including an electrocardiogram (ECG)component comprising a first, second, and third electrode wherein afirst ECG lead configured to generate a first ECG signal is formed uponuser contact with the first and second electrodes and wherein second andthird ECG leads configured to generate second and third ECG signals areformed upon user contact with the first, second, and third electrodes.The system further includes a scale component comprising a platformconfigured to support the user and a weight sensor in communication withthe platform and configured to generate a weight signal based on userpresence on the platform.

The system also includes a monitoring component comprising anon-transitory memory and a processor configured to execute instructionsstored in the non-transitory memory to receive the first, second, orthird ECG signal from the ECG component, receive the weight signal fromthe scale component, and combine features extracted from the first,second, or third ECG signal and the weight signal to generate a currentbiometric signal. Responsive to the current biometric signal matching ahistorical biometric signal, the monitoring component will obtain a userprofile and determine a health status for association with the userprofile by classifying the current biometric signal using disease modelsand fitness models.

Also disclosed herein is a monitoring component including anon-transitory memory and a processor configured to execute instructionsstored in the non-transitory memory to receive an electrocardiograph(ECG) signal from an ECG component in contact with a user, receive aweight signal from a scale component in contact with the user, andcombine features extracted from the ECG signal and the weight signal togenerate a current biometric signal. Responsive to the current biometricsignal matching a historical biometric signal, the processor is furtherconfigured to obtain a user profile and determine a health status forassociation with the user profile by classifying the current biometricsignal using disease models and fitness models.

Details of these implementations, modifications of theseimplementations, and additional implementations are described below.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIG. 1 shows a schematic illustration of a monitoring system.

FIG. 2 is a block diagram of a hardware configuration for the monitoringsystem of FIG. 1.

FIG. 3 is a flow chart showing an example of a process overview ofbiometric signal matching and health status generation.

FIG. 4 is a flow chart showing an example of a process of pre-processingbiometric signals.

FIG. 5 is a flow chart showing an example of a process of featureextraction for biometric signals.

FIG. 6 is a flow chart showing an example of a process of health statusdetermination based on biometric signals.

DETAILED DESCRIPTION

A monitoring system in the form of a biometric scale captures ECGsignals using electrodes and weight signals using a weight sensor forthe purposes of both identifying a user and providing a health status tothe user. Identification and health status are determined by comparingan analyzed version of the ECG and weight signals in the form of currentbiometric data to disease models, fitness models, and historicalbiometric data associated with a user profile. The analysis of thebiometric data can occur either directly at the biometric scale orremotely, for example, using a mobile device or a wearable device.

FIG. 1 shows a schematic illustration of a monitoring system 100. Themonitoring system 100 collects biometric data using contact-basedcommunication with a user's hands and feet for purposes of identifyingthe user and providing a health status to the user.

The monitoring system 100 includes an ECG component that can beselectively trained to identify specific ECG signals from the user, forexample, in order to identify stress levels or detect or diagnosespecific diseases or disease risk factors. To that end, the ECGcomponent includes first and second electrodes 102 and 104 disposed on ahandle 106 that are configured to measure various aspects of the user'sheart function and related biometrics through a touch input or contactwith the user's palms and/or fingers when the user grabs the handle 106.The first and second electrodes 102 and 104 are configured to identifyelectrical heart activity by measuring the user's pulse and transmittingthe ECG signal for subsequent encoding and processing. That is, upon theuser contacting both the first electrode 102 and the second electrode104, for example, with the palms or fingers of opposite hands, an ECGlead is formed, allowing the monitoring system 100 to measure the user'sheart activity. The first and second electrodes 102 and 104 canadditionally be configured to collect fingerprints or palm prints fromthe user for identification purposes.

The monitoring system 100 also includes a base 108 with another ECGcomponent comprising third and, optionally, fourth electrodes 110 and112 configured to measure various aspects of the user's heart functionand related biometrics through contact with the user's feet when theuser stands on the base 108 without socks or shoes. When the usercontacts both the third electrode 110 of the base 108 while at the sametime contacting the first electrode 102 and the second electrodes 104 onthe handle 106, a total of three ECG leads are formed, allowingdetection or diagnoses of additional diseases or disease risk factors.For example, the second of the ECG leads is based on voltage between theelectrode 104, generally in contact with the user's right fingers orpalm, and the electrode 110, generally in contact with the user's leftfoot. The third ECG lead is based on voltage between electrode 102,generally in contact with the user's left fingers or palm, and theelectrode 110, again, generally in contact with the user's left foot.

The base 108 can also include a scale component. The scale component caninclude a platform 114 configured to support the user and a weightsensor (not shown) in communication with the platform 114 and configuredto generate a weight signal based on user presence on the platform 114.The weight sensor can be a generally known device comprising load cells,pressure transducers, linear variable differential transformers,capacitance coupled sensors, or strain gages configured to convert theuser's physical weight into weight data that is representative of theuser's weight.

Though the various electrodes 102, 104, 110, 112 and the weight sensordescribed above are designed to capture and generate ECG data, weightdata, and optionally, fingerprint and palm print data, four types ofbiometric data, further signal detection components can be includedwithin the monitoring system 100. For example, the monitoring system 100can include a thermometer component comprising a temperature sensor (notshown) configured to measure the user's body temperature or a pulseoximeter (not shown) configured to measure the user's blood oxygenlevel. The various types of biometric data that can be captured andprocessed by the monitoring system 100 can be useful, for example, inestablishing identity of the user and tracking a health status oroverall fitness level for the user as described further below.

The monitoring system 100 can also include a display 116 configured tovisually represent collected biometric data. In one implementation, thedisplay 116 can be a single output screen for visually representing allcollected biometric data. For example, and as shown in FIG. 1, thedisplay 116 includes a single output screen that visually representsboth the user's weight in numerical form (e.g., W: 150 lbs.) and theuser's heart activity from the first, second, and third ECG leads ingraphical form. The information outputted to the display 116 may beupdated as additional biometric data is processed by the monitoringsystem 100.

In another implementation, the display 116 may be a plurality of outputscreens with each output screen visually representing a unique type ofcollected biometric data. Further, the biometric data captured by themonitoring system 100 can be sent to separate devices for processing ordisplay. For example, in another implementation, weight data and ECGdata can be outputted to a user on a display included within a mobiledevice such as a smart phone 118. In yet another implementation, weightdata and ECG data can be outputted to a user on a display includedwithin a wearable device such as a bracelet 120. Though a mobile deviceand a wearable device are given as examples, other devices can also bein communication with the monitoring system 100 to process or displayinformation associated with the biometric data collected using thevarious electrodes 102, 104, 110, 112 and weight sensors.

The monitoring system 100 can also include a monitoring component in theform of a computing device 122 configured to process and/or transmitbiometric data collected by the ECG component and the scale component ofthe monitoring system 100. Processing capabilities of the computingdevice 122 are described further below. The computing device 122 canalso be designed to transmit biometric data to separate devices, such asthe smart phone 118 or the bracelet 120 for processing and/or display.The computing device 122 can also be designed to transmit biometric datato a medical examiner for review, diagnosis of disease, or othertreatment purposes. The computing device 122 can also be designed totransmit biometric data to a database or other related system forstorage, such as for later review or comparison in the form ofhistorical biometric data. In one implementation, the computing device122 includes a Bluetooth transmitter; however, the computing device 122can communicate with other suitable wireless communication systems,including, without limitation, an ultrasound transmitter.

FIG. 2 is a block diagram of a hardware configuration 200 for themonitoring system 100 of FIG. 1. The hardware configuration 200 caninclude at least one processor such as central processing unit (CPU)202. Alternatively, CPU 202 can be any other type of device, or multipledevices, capable of manipulating or processing information now-existingor hereafter developed. Although the examples herein can be practicedwith a single processor as shown, advantages in speed and efficiency canbe achieved using more than one processor.

The hardware configuration 200 can include a memory 204 such as a randomaccess memory device (RAM), a read-only memory device (ROM), or anyother suitable type of storage device that stores code and data that canbe accessed by the CPU 202 using a bus 206. The code can include anoperating system and one or more application programs processing and/oroutputting the biometric data for the monitoring system 100. Anapplication program can include software components in the form ofcomputer executable program instructions that cause the CPU 202 toperform some or all of the operations and methods described herein.

The hardware configuration 200 can optionally include a storage device208 in the form of any suitable non-transitory computer readable medium,such as a hard disc drive, a memory device, a flash drive or an opticaldrive. The storage device 208, when present, can provide additionalmemory when high processing requirements exist. The storage device 208can also store any form of data whether relating to or not relating tobiometric data.

The hardware configuration 200 can include one or more input devices210, such as a keyboard, a numerical keypad, a mouse, a microphone, atouch screen, a sensor, or a gesture-sensitive input device. Through theinput device 210, data can be input from the user or another device. Forexample, a gesture-sensitive input device can receive different gesturesto switch between different display modes (e.g., heart rate, weight,ECG, etc.). The input device 210 can also be any other type of inputdevice including an input device not requiring user intervention. Forexample, the input device 210 can be a communication device such as awireless receiver operating according to any wireless protocol forreceiving signals. The input device 210 can also output signals or data,indicative of the inputs, to the CPU 202 using the bus 206.

The hardware configuration 200 can also include one or more outputdevices 212. The output device 212 can be any device transmitting avisual, acoustic, or tactile signal to the user, such as a display, atouch screen, a speaker, an earphone, a light-emitting diode (LED)indicator, or a vibration motor. If the output device 212 is a display,for example, it can be a liquid crystal display (LCD), a cathode-raytube (CRT), or any other output device capable of providing visibleoutput to the user. In some cases, the output device 212 can alsofunction as an input device 210, for example, when a touch screendisplay is configured to receive touch-based input. The output device212 can alternatively or additionally be formed of a communicationdevice for transmitting signals. For example, the output device 212 caninclude the computing device 122 described in association with themonitoring system 100 in FIG. 1.

Although FIG. 2 depicts one hardware configuration 200 that canimplement the monitoring system 100, other configurations can be used.The operations of the CPU 202 can be distributed across multiplemachines or devices (each machine or device having one or moreprocessors) that can be coupled directly or across a local area or othernetwork. The memory 204 can be distributed across multiple machines ordevices such as network-based memory or memory in multiple machinesperforming operations that can be described herein as being performedusing a single computer or computing device for ease of explanation.Although a single bus 206 is depicted, multiple buses can be used.Further, the storage device 208 can be a component of the hardwareconfiguration 200 or can be a shared device that is accessed via anetwork. Thus, the hardware configuration 200 as depicted in FIG. 2 canbe implemented in a wide variety of configurations.

FIG. 3 is a flow chart showing an example of a method 300 of biometricsignal matching and health status generation. The operations describedin connection with method 300 can be performed using the monitoringcomponent of the monitoring system 100. The monitoring component can be,for example, the computing device 122, the smart phone 118, thebracelet120, a remote server (not shown), or the cloud (not shown). Theoperations described in connection with the method 300 can be embodiedas a storage device in the form of a non-transitory computer readablestorage medium including program instructions executable by one or moreprocessors that, when executed, cause the one or more processors toperform the operations of the method 300 described below.

At operation 302, one or more ECG signals are received as captured by acombination of the first, second, third, or fourth electrodes 102, 104,110, 112 of the ECG component based on the user gripping the handle 106and/or standing barefoot on the platform 114 of the monitoring system100. At operation 304, a weight signal is received as captured by theweight sensor of the scale component based on the user standing on theplatform 114 of the monitoring system 100. Other signals can also becaptured by other sensors associated with the monitoring system 100. Forexample, the user's pulse oxygen level, body temperature, fingerprints,or palm prints can be captured while the user grips the handle 106and/or stands barefoot on the platform 114.

Both the ECG signals and the weight signals (as well as any othersignals captured) typically comprise raw data and need to be processedin order to be properly used to analyze the user's health, for example,to determine disease diagnoses, stress levels, or readiness forexercise. In one implementation, specific ECG signals may be selectivelymeasured based on training provided by the ECG component of themonitoring system 100. At operation 306, the identified ECG and weightsignals separately undergo signal pre-processing and feature extractionto determine various features thereof. These processes are described inreference to FIGS. 4 and 5.

FIG. 4 is a flow chart showing an example of a method 400 forpre-processing each of the ECG and weight signals. At sub-operation 402,a baseline wander, if present, is removed from the ECG and weightsignals. At sub-operation 404, a band-pass filter is applied to the ECGand weight signals in order to remove any undesirable data shifts thatoccurred while the signals were being measured and to reduce thepresence of data outside of a range to be observed (e.g., outliers).

An adaptive motion noise reduction filter is applied at sub-operation406 that filters identified motion noise included within the ECG andweight signals and reduces the motion noise or entirely removes it tobetter isolate the important data within those signals. Motion noise mayinclude, for example, fluxes and other changes present in the ECG andweight signals due to the user wiggling or otherwise moving in a mannerthat may interfere with a clear biometric measurement (e.g., where theuser's finger moves on second electrode 104 or the user's foot moves onthe third electrode 110 while the ECG signals are being measured). Thefilter adapts to the specific form of the ECG and weight signals.

FIG. 5 is a flow chart showing an example of a method 500 for performingfeature extraction on the biometric signals. Initially, at sub-operation502, the denoised signals are identified, as processed and outputtedfrom the preceding sub-operations 402, 404, and 406 of the method 400.At sub-operation 504, the QRS complex features of the biometric signalsare identified to determine the graphical deflections (e.g., wherein Qand S are valleys and R is a peak) representative of the depolarization,for example, of the left and right ventricles of the user's heart. Oncethe QRS complex features are identified, one or more of three differentfeature extraction operations may be performed.

At sub-operation 506, the method 500 can be used to identify wavelettransformation features of the denoised biometric signals. Atsub-operation 508, other wave features, such as wave magnitude featuresincluding various fiducial point features relative to the QRS complexfeatures can be identified. At sub-operation 510, periodicity intervalfeatures relative to the QRS complex features and related wave featurescan be identified. The method 500 can be completed by performing justone of the sub-operations 506, 508, 510, by performing a combination ofany two of the sub-operations 506, 508, 510, or by performing all threesub-operations 506, 508, 510.

The above-described wavelet transformation features determined insub-operation 506 can be identified along with other frequency domainfeatures (including, without limitation, auto-correlation discretecosine transform features), which may be identified directly from theprocessed and denoised biometric signals. That is, in oneimplementation, the wavelet transformation features and other frequencydomain features may be identified separately from the temporal domainfeatures of the biometric signals (e.g., wave magnitude features,periodicity interval features, and other fiducial point features), whichtemporal domain features are identified, for example, based on theidentification and detection of the QRS complex features insub-operation 504.

Notwithstanding the foregoing, it is likely that the most accurateresults for the method 300 are obtained by performing all three of thesub-operations 506, 508, 510. In one implementation, the extractedfeatures are normalized after they are identified so that the biometricsignals may subsequently be compared based on the same periodicity. Theextracted features can be used, for example, to detect a specificdisease or fitness condition based on certain rules.

Returning to FIG. 3, at operation 308, an interrelationship between thevarious features of the pre-processed ECG and weight signals isdetermined by checking the features against each other. The extractedfeatures of the ECG and weight signals are then merged into a singlecurrent biometric signal for further processing and analysis, which willpermit the subsequent operations of the method 300 to yield moreaccurate results than if the operations were performed separately on thevarious features of the ECG and weight signals.

At decision-tree 310, a multi-modal decision fusion can be used todetermine whether the identity of the user currently providing input tothe monitoring system 100 is known based on a comparison of the currentbiometric signal generated during the preceding operations to historicalbiometric signals associated with existing user profiles. Ideally,biometric characteristics are unique in that no two individuals haveidentical measurements and are permanent in that the characteristics donot change over time.

However, certain types of biometric characteristics, such asmeasurements identified via ECG signals, may be insufficient when usedalone to determine a user's identity, as the measurements may onlyidentify certain qualities of the characteristic, which may be common inmany individuals. By using a combination of ECG signals with weightsignals, and optionally, other biometric signals based on the user'sbody temperature, pulse oxygen level, fingerprints, or palm prints, asprovided by the user in the form of a current biometric signal, theidentification described herein combines different biometric signalsfrom different sensors to more accurately determine the user's identity.

If the current biometric signal matches a historical biometric signal,for example, by comparing the current biometric signal to historicalbiometric signals stored with user profiles for the monitoring system100, the user's identity may be verified, and the method 300 continuesto operation 312 where the user's profile is obtained. If the currentbiometric signal does not match any historical biometric signals, themethod 300 continues to operation 314, and a profile generation requestsoliciting the user to generate a user profile for the monitoring system100 is sent to the user. Information related to the profile generationrequest may be displayed to the user, for example, using the display116, the smart phone 118, or the bracelet120. The user can respond tothe profile generation request by providing information to generate anew profile, which in turn can allow the method 300 to proceed tooperation 312 where the new user profile is obtained.

At operation 316, a health status for association with the user profileis determined by machine learning algorithms, such as by classifying thefeatures extracted from the current biometric signal using previouslytrained disease and fitness models. Disease models allow identificationof disease risk factors and/or disease diagnoses for the user based onthe current biometric signal. Fitness models allow identification offatigue levels, stress levels, etc. related to exercise readiness of theuser based on the current biometric signal. A more detailed descriptionof how risk factors and disease diagnoses are determined using thecurrent biometric signal is described in reference to FIG. 6.

FIG. 6 is a flow chart showing an example of a method 600 of disease andfitness prediction. At sub-operation 602, pre-processing and featureextraction similar to that described above with respect to FIGS. 4 and 5may be used to refine the ECG and weight signals prior to classifyingthe same using disease and fitness models. Different disease and fitnessmodels may be used for the classification. For example, if the user isknown to have a disease or fitness condition related to the underlyingbiometric data, for example, heart disease, obesity, or low heart-ratevariability, this information can be used in the subsequent analysis,for example, by analyzing the current biometric signal againsthistorical biometric signals collected from the user.

At sub-operation 604, a current biometric signal is generated using theextracted features of the ECG and weight signals, allowing thesubsequent operations of the method 600 to yield more accurate resultsthan if the operations were performed separately on the various featuresof the ECG and weight signals.

At sub-operation 606, the current biometric signal is classified usingdisease and fitness models. This classification determines thelikelihood of certain diseases or fitness conditions or the presencethereof. For example, the analysis of the current biometric signal maybe used to predict and/or diagnosis the user with obesity, high stress,advanced heart age, low heart rate variability, or other medicalconditions such as previous heart attack, congestive heart failure,chronic obstructive pulmonary disease (COPD), anemia, lung cancer,asthma, or pneumonia.

At sub-operation 608, the classification can be further supported bydetermining a health status trend by comparing the classificationdetermined using the current biometric signal to classificationsdetermined using historical biometric signals in order to providefurther insight for indicating disease factors, fitness factors, ordisease diagnoses for the user. For example, if the user has previouslybeen diagnosed with a relevant disease, fitness condition, or medicalcondition, the comparison between the current classification and thehistorical classification can be used to identify the user's treatmentprogress and other developments in treatment.

Based on the results of the analyses, classifications, and comparisonsin sub-operations 606 and 608, an overall prediction of health statusfor the user, including disease conditions and fitness conditions, canbe determined at sub-operation 610. The health status can includeinformation related to the user's weight, heart rate, fatigue level,stress level, heart age, heart rate variability, or heart conditionbased on classification of the user's current biometric signal, andoptionally, the user's historical biometric signals, using variousdisease models and fitness models.

Returning to FIG. 3, at operation 318, the method 300 includesdisplaying a status indicator associated with the health status to theuser. The status indicator can be shown to the user, for example, usingthe display 116, the smart phone 118, or the bracelet120. Other devicescan also provide the status indicator to the user. Non-limiting examplesof the content represented by the status indicator can include diagnosesor conditions related to the user's weight, heart rate variability,stress level, heart age, or other various heart diseases such as sinustachycardia, sinus bradycardia, sinus arrhythmia, sinoatrial exit block,atrial fibrillation, atrial flutter, multifocal atrial tachycardia,wandering atrial pacemaker, ectopic atrial rhythms, atrioventricularnodal reentry tachycardia, premature ventricular contractions,ventricular fibrillation, asystole, junctional rhythms, left anteriorfascicular block, etc. After operation 318, the method 300 ends.

While the disclosure has been described in connection with certainembodiments and implementations, it is to be understood that theinvention is not to be limited to the disclosed embodiments but, on thecontrary, is intended to cover various modifications and equivalentarrangements included within the scope of the appended claims, whichscope is to be accorded the broadest interpretation so as to encompassall such modifications and equivalent structures as is permitted underthe law.

What is claimed is:
 1. A system, comprising: an electrocardiogram (ECG)component, comprising: a first electrode; a second electrode, wherein afirst ECG lead configured to generate a first ECG signal is formed uponuser contact with the first and second electrodes; and a thirdelectrode, wherein second and third ECG leads configured to generatesecond and third ECG signals are formed upon user contact with thefirst, second, and third electrodes; a scale component, comprising: aplatform configured to support the user; a weight sensor incommunication with the platform and configured to generate a weightsignal based on user presence on the platform; and a monitoringcomponent, comprising: a non-transitory memory; and a processorconfigured to execute instructions stored in the non-transitory memoryto: receive the first, second, or third ECG signal from the ECGcomponent; receive the weight signal from the scale component; combinefeatures extracted from the first, second, or third ECG signal and theweight signal to generate a current biometric signal; responsive to thecurrent biometric signal matching a historical biometric signal, obtaina user profile; and determine a health status for association with theuser profile by classifying the current biometric signal using diseasemodels and fitness models.
 2. The system of claim 1, wherein theprocessor is further configured to: responsive to the current biometricsignal not matching a historical biometric signal, send a profilegeneration request soliciting the user to generate a user profile. 3.The system of claim 1, wherein receiving the first, second, or third ECGsignals comprises receiving a touch input from the user at two, three,or four of the electrodes of the ECG component.
 4. The system of claim1, wherein the health status is related to the user's weight, heartrate, fatigue level, stress level, heart age, heart rate variability, orheart condition.
 5. The system of claim 1, wherein determining thehealth status further comprises determining a health status trend bycomparing the health status determined using the current biometricsignal to a health status determined using the historical biometricsignal.
 6. The system of claim 1, wherein the processor is furtherconfigured to: display, at an output screen associated with themonitoring component, a status indicator associated with the healthstatus.